The present disclosure relates generally to computed tomography (CT) imaging and inspection systems and, more particularly, to a method for iterative CT reconstruction using multi-modal edge information.
In present date industrial inspection processes, different types of measurement systems are available such as computed tomography (CT), coordinate measuring machines (CMM), laser-based profilometry, etc. Each inspection modality has its own advantages and disadvantages associated therewith. Modalities such as CMM and laser-based profilometry can measure external surfaces with high accuracy, but cannot measure internal features unless the part is cut open. To date, CT is the most versatile of the measurement/inspection systems for revealing both the internal and external structures of industrial parts in a non-destructive manner. The potential industrial applications of CT include reverse engineering, rapid prototyping, casting simulation & validation, tire development, first article inspection, ceramic porosity inspection, process validation, parts qualification and defect detection, to name a few. However, the low inspection accuracy of CT inhibits widespread applications thereof.
For example, in the area of reverse engineering, CT is a poor choice for capturing detailed external surface features, which can be crucial for capturing the design intent. The factors affecting CT accuracy in this regard include (among other aspects) beam-hardening, partial volume effect, scattering and off-focal radiation. Thus, in order to improve CT inspection accuracy, more effective methods are needed for removing the effects of these artifacts. In the area of CT image reconstruction, filtered backprojection (FBP) is a common technique because of its fast computation and ease of implementation. However, because FBP oversimplifies the CT data acquisition into an ideal Radon transform (i.e., Fan Beam transform, cone beam transform or any other transform depending on the particular acquisition geometry), the reconstructed image suffers from artifacts such as beam hardening and partial volume as discussed above, contributing to degraded image quality and inspection accuracy. Precorrection of the data prior to performing an FBP reconstruction is generally not capable of fully correcting for the artifacts due to these effects.
Another type of CT image reconstruction methodology is what is referred to as iterative reconstruction. These techniques are based on different mathematical principles, such as the statistical approach of maximum likelihood, and the least squares approach, for example. Iterative methods allow for the incorporation of a dedicated forward model of the data acquisition. On the other hand, because the computation associated with iterative reconstruction is highly intensive, iterative methods are not yet widely used in CT. As such, in present inspection processes, CT images are typically reconstructed using the FBP method where both external and internal features are extracted from the CT images. In the event a more accurate measurement of internal features is desired, the part is generally cut open and inspected by CMM. Thereafter, the CT measurement is calibrated using the CMM measurement, so as to compensate for any possible bias. Unfortunately, this procedure is both time-consuming and expensive.
Accordingly, it is desirable to be able to provide an improved inspection method that accurately captures both the internal and external features of an object to be inspected, but in a time efficient and inexpensive manner.